eightforge-performance-reviewer

Reviews EightForge code changes for performance, scalability, and correctness-preserving optimization.

Updated Mar 8, 2026
One-click install
npx skills add https://github.com/EricAnalysis/eightforge-os --skill eightforge-performance-reviewer-ericanalysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eightforge-performance-reviewer
Source: https://github.com/EricAnalysis/eightforge-os/tree/main/.cursor/skills/eightforge-performance-reviewer
Command: npx skills add https://github.com/EricAnalysis/eightforge-os --skill eightforge-performance-reviewer-ericanalysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Large spreadsheets, dense PDFs, and multi-document pipelines can make data-heavy applications slow, memory-hungry, or silently lossy under load. This Skill provides a structured review lens that catches performance regressions, unbounded queries, memory explosions, and lossy optimizations before they ship. ## Core Features & Use Cases - Performance Review Checklist: Evaluates query fan-out, pagination, caching, background workers, timeouts, retries, and rendering costs against explicit non-negotiable rules. - Authority Classification: Classifies every reviewed path as production authority, compatibility, shadow, design target, or superseded before drawing performance conclusions. - Structured Verdict Output: Produces a Pass / Pass with Concerns / Fail verdict with severity-ordered issues, minimal fixes, regression risks, and suggested tests. - Use Case: When a PR adds a new extraction pipeline or a Supabase-heavy API route, run this review to verify bounded reads, honest partial/failure states, and measured hot paths before merging. ## Quick Start Ask the AI to review the current pull request with the eightforge-performance-reviewer skill and report any scalability or lossy-optimization issues.

Frequently Asked Questions about eightforge-performance-reviewer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review a pull request for performance issues?▼

Invoke the performance reviewer on the changed files and it will classify each path, check the non-negotiable rules, and walk the review checklist. It returns a Pass, Pass with Concerns, or Fail verdict with severity-ordered issues and minimal fixes.

What performance problems does this review catch in Supabase queries?▼

It flags unbounded row reads, missing pagination, unselective filters, and hot paths where RLS predicate cost has no measured query plan. Deep RLS policy correctness itself is routed to the dedicated Supabase reviewer.

Can this reviewer be used with Next.js server components?▼

Yes, it evaluates Server Components versus client hydration choices for data-heavy surfaces and checks that React lists avoid full re-renders of large structures. It also verifies API routes bound work with timeouts and batch sizes.

What counts as a lossy optimization during review?▼

Lossy optimizations include silent row truncation, silent sampling, dropped evidence dependencies, weakened tenant isolation, bypassed freshness, and replacing durable state with an unauditable cache. Any of these causes a Fail verdict regardless of speed gains.

When should I not use the performance reviewer?▼

Route extraction completeness to the document intelligence reviewer, RLS and query-policy correctness to the Supabase reviewer, truth and freshness authority to the truth engine reviewer, and interaction usability to the UX reviewer. This reviewer owns correctness-preserving scale only.